How many human proteoforms are there?

Aebersold, Ruedi; Agar, Jeffrey N.; Amster, I. Jonathan; Baker, Mark S.; Bertozzi, Carolyn R.; Boja, Emily S.; Costello, Catherine E.; Cravatt, Benjamin F.; Fenselau, Catherine; Garcia, Benjamin A.; Ge, Ying; Gunawardena, Jeremy; Hendrickson, Ronald C.; Hergenrother, Paul J.; Huber, Christian G.; Ivanov, Alexander R.; Jensen, Ole N.; Jewett, Michael C.; Kelleher, Neil L.; Kiessling, Laura L.; Krogan, Nevan J.; Larsen, Martin R.; Loo, Joseph A.; Loo, Rachel R. Ogorzalek; Lundberg, Emma; MacCoss, Michael J.; Mallick, Parag; Mootha, Vamsi K.; Mrksich, Milan; Muir, Tom W.; Patrie, Steven M.; Pesavento, James J.; Pitteri, Sharon J.; Rodriguez, Henry; Saghatelian, Alan; Sandoval, Wendy; Schlüter, Hartmut; Sechi, Salvatore; Slavoff, Sarah A.; Smith, Lloyd M.; Snyder, M; Thomas, Paul M.; Uhlén, Mathias; Eyk, Jennifer E. Van; Vidal, Marc; Walt, David R.; White, Forest M.; Williams, Evan R.; Wohlschlager, Therese; Wysocki, Vicki H.; Yates, Nathan A.; Young, Nicolas L.; Zhang, Bing

doi:10.1038/nchembio.2576

Cited by 730 publications

(606 citation statements)

References 91 publications

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“…Advances in MS instrumentation have permitted the field to delve into increasingly more complex questions. With a draft of the complete human proteome presented, researchers now complement this work through top‐down proteoform analysis, examining protein comodifications to provide greater functional insights . Protein chaperone networks, including conformational analysis is accessible through MS, as is quantifying proteins from diminishing amounts of starting material (ie, nanoproteomics) .…”

Section: The Proteomics Facility: Instrumentationmentioning

confidence: 99%

Developing front‐end devices for improved sample preparation in MS‐based proteome analysis

Doucette

Nickerson

2020

J Mass Spectrom

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Chemical analysis has long relied on instrumentation, from the simplest (eg, burets) to the more sophisticated (eg, mass spectrometers) to facilitate precision measurements. Regardless of their complexity, the development of a new instrumental device can be a valued approach to address problems in science. In this perspective, we outline the process of novel device design, from early phase conception to the manufacturing and testing of the tool or gadget. Focus is placed on the development of improved front‐end devices to facilitate protein sample manipulations ahead of mass spectrometry, which therefore augment the proteomics workflow. Highlighted are some of the many training secrets, choices, and challenges that are inherent to the often iterative process of device design. In hopes of inspiring others to pursue instrument design to address relevant research questions, we present a summary list of points to consider prior to innovating their own devices.

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Section: The Proteomics Facility: Instrumentationmentioning

confidence: 99%

Developing front‐end devices for improved sample preparation in MS‐based proteome analysis

Doucette

Nickerson

2020

J Mass Spectrom

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“…These include classification of proteins into functional annotations [3][4][5] , subcellular localization and organismal levels [6][7][8] , and classification of domains and functional sites [9][10][11] . These characterizations generated a unified language now applied by the scientific community, allowing better communication and resulting in advances the relevant field of research.…”

Section: Introductionmentioning

confidence: 99%

Conservation motifs - a novel evolutionary-based classification of proteins

Beer

Sherill-Rofe

Unterman

et al. 2020

Preprint

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Cross-species protein conservation patterns, as directed by natural selection, are indicative of the interplay between protein function, protein-protein interaction and evolution. Since the beginning of the genomic era, proteins were characterized as either conserved or not conserved. This simple classification became archaic and cursory once data on protein orthologs became available for thousands of species.To enrich the language used to describe protein conservation patterns, and to understand their biological significance, we classified 20,294 human proteins against 1096 species. Analyses of the conservation patterns of human proteins in different eukaryotic clades yielded extremely variable and rich patterns that had never been characterized or studied before. Using mathematical classifications, we defined seven conservation motifs: Steps, Critical, Lately Developed, Plateau, Clade Loss, Trait Loss and Gain, which describe the evolution of human proteins.One type of motif, which we termed Gain, describes the human proteins that are highly conserved in a small number of organisms but are not found in most other species.Interestingly, this pattern predicts 73 possible instances of horizontal gene transfer in eukaryotes.Overall, our work offers novel terms for conservation patterns and defines a new language intended to classify proteins based on evolution, reveal aspects of protein evolution, and improve the understanding of protein functions.

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“…2 And these compounds act in biological systems of millions or more protein species or "proteoforms" (considering genetic mutations, alternative splicing, and post-translation modifications of proteins). 3,4 Facing such a combinatorial explosion of compound-protein pairs, drug discovery calls for efficient characterization of compound efficacy and toxicity, and computational prediction of compound-protein interactions (CPI) addresses the need.…”

Section: Introductionmentioning

confidence: 99%

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

Karimi

Wang

et al. 2019

Preprint

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Predicting compound-protein affinity is critical for accelerating drug discovery.Recent progress made by machine learning focuses on accuracy but leaves much to be desired for interpretability. Through molecular contacts underlying affinities, our large-scale interpretability assessment finds commonly-used attention mechanisms inadequate. We thus formulate a hierarchical multi-objective learning problem whose predicted contacts form the basis for predicted affinities. We further design a physicsinspired deep relational network, DeepRelations, with intrinsically explainable architecture. Specifically, various atomic-level contacts or "relations" lead to molecular-level affinity prediction. And the embedded attentions are regularized with predicted structural contexts and supervised with partially available training contacts. DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively. Our study represents the first dedicated model development and systematic model assessment for interpretable machine learning of compound-protein affinity.

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How many human proteoforms are there?

Cited by 730 publications

References 91 publications

Developing front‐end devices for improved sample preparation in MS‐based proteome analysis

Developing front‐end devices for improved sample preparation in MS‐based proteome analysis

Conservation motifs - a novel evolutionary-based classification of proteins

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

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